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multi-objective particle swarm optimization algorithm for the Minimum Constraint Removal Problem

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2020年第1期13卷 291-299页

作者： Xu, Bo Feng, Zhou Gates, Antonio Marcel Guangdong Univ Finance & Econ Sch Informat Sci Guangzhou 510320 Peoples R China Hawaii Pacific Univ Honolulu HI 96813 USA

This paper proposes a multi-objective approach for the minimum constraint removal (MCR) A problem. First, a multi-objective model for MCR path planning is constructed. This model takes into account factors such as the minimum constraint set, the route length, and the cost. A multi-objective particle swarm optimization (MOPSO) algorithm is then designed based on the fitness function of the multi-objective MCR problem, and an iteration formula based on the personal best (pbest) and global best (gbest) of the algorithm is constructed to update the particle velocity and position. Finally, compared with ant colony optimization (ACO) A and the crow search algorithm (CSA) A, the experimental results show that the MOPSO-based path planning algorithm can find a shorter path that traverses fewer obstacle areas and can thus perform MCR path planning more effectively. (C) 2020 The Authors. Published by Atlantis Press SARL.

关键词： Minimum constraint removal Minimum constraint set Path planning multi-objective optimization multi-objective particle swarm optimization algorithm

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multi-objective particle swarm optimization algorithm for multi-Step Electric Load Forecasting

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ENERGIES 2020年第3期13卷 532-532页

作者： Yang, Yi Shang, Zhihao Chen, Yao Chen, Yanhua Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Free Univ Berlin Dept Math & Comp Sci D-14195 Berlin Germany Zhengzhou Univ Sch Informat Engn Zhengzhou 450000 Peoples R China

As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability.

关键词： electric load forecasting extreme learning machine recurrent neural network support vector machines multi-objective particle swarm optimization algorithm

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Inventory Control Method of Reverse Logistics for Shipping Electronic Commerce Based on Improved multi-objective particle swarm optimization algorithm

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JOURNAL OF COASTAL RESEARCH 2018年第Spe83期34卷 786-790页

作者： Yang, Wei Xie, Qianglai Li, Ming Jiangxi Univ Technol Nanchang 330069 Jiangxi Peoples R China

Based on the improved multi-objective particle swarm algorithm, a method of inventory control of reverse logistics for shipping electronic commerce was proposed. Through the multi-objective particle swarm optimization algorithm, the inventory of electronic commerce was distributed well, heuristic algorithm was used to get the optimal solution set conforming to the preference of decision maker. According to the target weights given by decision-maker, the utility evaluation of the optimal solution set was carried out. The maximum solution of utility evaluation value was selected as the optimal scheme to complete the inventory control of reverse logistics for shipping electronic commerce. Experimental results show that the proposed method can effectively complete the inventory control of reverse logistics for shipping electronic commerce.

关键词： multi-objective particle swarm optimization algorithm reverse logistics inventory control electronic commerce

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Adaptive vehicle extraction in real-time traffic video monitoring based on the fusion of multi-objective particle swarm optimization algorithm

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EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2018年第1期2018卷 145页

作者： Yu, Shijun Deng, Shejun Yangzhou Univ Coll Civil Sci & Engn 198 Huayang Rd Yangzhou 225127 Jiangsu Peoples R China

In view of the problems in the real-time traffic video monitoring that the adaptive vehicle extraction is greatly affected by the environmental factors such as the illumination, noise, and so on;the missed detection and error detection rate is high;and it is difficult to meet the robustness and the real-time performance at the same time, a kind of method for the adaptive vehicle extraction in real-time traffic video monitoring based on the fusion of multi-objective particle swarm optimization algorithm is put forward. In this method, based on the multi-objective particle swarm optimization algorithm, adaptive binarization processing is carried out on the image first, and the interference points are removed by filtration through the erosion and expansion method. Simple and effective method is used to carry out the merger of the shadow line and the extraction of the real-time traffic video. In the algorithm, the information entropy in the target area and the symmetry characteristics of the vehicle tail are used to screen and identify the region of interest, which has reduced the missed detection and error detection rate of the algorithm. The multi-objective particle swarm optimization algorithm is used to extract the vehicle boundaries and has achieved relatively good effect. The results show that the detection accuracy is 89% and the average operating speed is 17.6 frames/s during the processing of the real-time traffic video with the resolution of 640x480.

关键词： Vehicle detection multi-objective particle swarm optimization algorithm Real-time traffic video Adaptive vehicle extraction

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A multi-objective Chaotic particle swarm optimization algorithm Based on Improved Inertial Weights

A Multi-Objective Chaotic Particle Swarm Optimization Algori...

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2018 International Conference on Computer,Communication and Network Technology(CCNT 2018)

作者： Zhi-yuan PAN Da-min ZHANG Dong LIU Jun YANG Juan-min CHEN College of Data and Information engineering Guizhou University

In order to enhance the convergence and distribution of multi-objective particle swarm algorithm, an improved multi-objective particle swarm optimization algorithm was proposed. Linear decreasing inertia weight was used to update. The method can improve the deficiency that the algorithm falls into local optimal easily. The improved Logistic mapping was used to increase ergodicity of the particles. The method can expand the search scope. At the same time, the elite archiving mechanism and the mutation probability were introduced to increase the disturbance. The method can improve the local optimal. Compared with the real Pareto front, NSGA-II and MOEAD algorithm, the simulation shows that the algorithm proposed in the paper is effective.

关键词： multi-objective particle swarm optimization algorithm Logistic map Elite archiving mechanism Mutation probability

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Reliability-aware web service composition with cost minimization perspective: a multi-objective particle swarm optimization model in multi-cloud scenarios

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SOFT COMPUTING 2024年第6期28卷 5173-5196页

作者： Tabalvandani, Mohammad Ali Nezafat Shirvani, Mirsaeid Hosseini Motameni, Homayun Islamic Azad Univ Sari Branch Dept Comp Engn Sari Iran

The multi-cloud environment (MCE) serves users on-demand by presenting miscellaneous online web services. Each web service which is delivered by every cloud provider has its own quality of features and also own pricing scheme. In the web service composition technology, the integration of the services required by the users is done with the aim of producing the efficient solutions with the desired quality. In some businesses, continuity of activities is very important and a business that fails a lot cannot be trusted by subscribers. In these businesses, it is necessary to maximize the reliability of the system along with minimizing the overall monetary costs. To this end, two new reliability and cost models are presented. All of the network equipment, communication, and elements affecting the total cost and reliability of the system are taken into consideration in the proposed models. Then, the web service composition issue is formulated to a multi-objective optimization problem. To solve this combinatorial problem in large search space of MCE, the multi-objective particle swarm optimization algorithm is suggested to maximize reliability while minimizing the cost of services and make Pareto optimal points. The results of the evaluations show that in different scenarios, the proposed solution proves the amount of 48%, 46%, and 12% averagely improvement over other comparative MOGWO, NSGA-II, and MOEA/D approaches in terms of service failure rate, service implementation cost in cloud providers, and the execution time respectively.

关键词： Web service composition multi-cloud environment Reliability model Cloud cost model multi-objective optimization problem multi-objective particle swarm optimization algorithm

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A Dual-Level optimization Scheduling Method for Smart Distribution Networks with multiple Flexible Resource Interactions Based on multi-Strategy Enhanced particle swarm optimization algorithm 4

A Dual-Level Optimization Scheduling Method for Smart Distri...

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4th International Conference on Smart Grid and Energy Internet, SGEI 2024

作者： Chen, Fengchao Qiu, Zejian Li, Qiwei Zhao, Ruifeng Lai, Weijian Dongguan Power Supply Bureau Guangdong Power Grid Co. Ltd Guangdong Dongguan523008 China Electric Power Dispatching and Control Center Guangdong Power Grid Co. Ltd Guangzhou510600 China

ISBN: (纸本)9798350368314

The rapid development of distributed renewable energy systems is swiftly transforming the structure of distribution networks. An essential issue in smart distribution networks is how to achieve interactive optimization among various entities with different forms. Against this backdrop, this paper investigates the regulation system and optimization scheduling algorithm for smart distribution networks with a high proportion of distributed generation. Firstly, it analyses the scheduling characteristics of various adjustable resources within smart distribution networks and examines the output characteristics of distributed energy sources, establishing an interactive model of "source-load-storage" that considers multiple adjustable resources. Secondly, it incorporates the topology of the distribution network into the day-ahead scheduling system, establishing a bi-level optimization scheduling model that considers dynamic network reconfiguration. The upper-level model is a dynamic reconfiguration model of the distribution network, aiming to maximize both the economic efficiency of scheduling and dynamic reconfiguration. The lower-level model is a day-ahead economic dispatch model for the distribution network, considering "source-load" coordination, with the goal of maximizing day-ahead scheduling economic efficiency. Subsequently, a multi-objective particle swarm optimization algorithm based on the three black holes capture strategy is used to solve this model. Finally, the IEEE 33-bus distribution system is used as a case study. The simulation results demonstrate that the bi-level optimization model can significantly enhance the economic efficiency and reliability of distribution network operations. ©2024 IEEE.

关键词： distributed renewable energy systems multi-objective particle swarm optimization algorithm Smart distribution networks source-load-storage

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Chaotic image encryption algorithm based on hybrid multi-objective particle swarm optimization and DNA sequence

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OPTICS AND LASERS IN ENGINEERING 2021年 137卷 106393-106393页

作者： Wang, Xingyuan Li, Yanpei Dalian Maritime Univ Sch Informat Sci & Technol Dalian 116026 Peoples R China

This paper studies an image encryption algorithm based on multi-objective particle swarm optimization (MOPSO), DNA encoding sequence and one-dimensional Logistic map. First, the key of this paper consists of the sub-key sequence selected by particle swarm optimization (PSO), a hash value of the plaintext image, and a shuffle mark bit. Create random DNA mask images using Logistic map and DNA encoding. Then use it and the block-shuffled plaintext DNA encoding sequence to operate to form an encryption system. In PSO, the position value of a particle represents a position of the plaintext image, the iterative PSO algorithm is based on the information entropy and correlation coefficient. Finally, obtains the best ciphertext, and returns the value of the best particle at this time. Simulation experiment and security analysis show that the correlation coefficient and entropy of ciphertext are excellent, and it can resist all kinds of typical attacks and has better encryption effect.

关键词： Image encryption multi-objective particle swarm optimization algorithm DNA encoding sequence Logistic map

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Optimal energy management of microgrid using advanced multi-objective particle swarm optimization

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ENGINEERING COMPUTATIONS 2020年第6期37卷 2085-2110页

作者： Anh, Ho Pham Huy Kien, Cao Van Ho Chi Minh City Univ Technol VNU HCM FEEE Ho Chi Minh City Vietnam Ind Univ Ho Chi Minh City Fac Elect Technol Ho Chi Minh City Vietnam

Purpose The purpose of this paper is to propose an optimal energy management (OEM) method using intelligent optimization techniques applied to implement an optimally hybrid heat and power isolated microgrid. The microgrid investigated combines renewable and conventional power generation. Design/methodology/approach Five bio-inspired optimization methods include an advanced proposed multi-objective particle swarm optimization (MOPSO) approach which is comparatively applied for OEM of the implemented microgrid with other bio-inspired optimization approaches via their comparative simulation results. Findings Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization methods. Moreover, the proposed MOPSO is successfully applied to perform 24-h OEM microgrid. The simulation results also display the merits of the real time optimization along with the arbitrary of users' selection as to satisfy their power requirement. Originality/value This paper focuses on the OEM of a designed microgrid using a newly proposed modified MOPSO algorithm. Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization approaches.

关键词： Optimal energy management Hybrid microgrid multi-objective genetic algorithm multi-objective particle swarm optimization algorithm 24-hour optimal energy management simulations Bio-inspired optimization methods

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New hybrid between NSGA-III with multi-objective particle swarm optimization to multi-objective robust optimization design for Powertrain mount system of electric vehicles

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ADVANCES IN MECHANICAL ENGINEERING 2020年第2期12卷

作者： Nguyen Huy Truong Dinh-Nam Dao Inst Mil Mech Engn Hanoi Vietnam Le Quy Don Tech Univ Control Technol Coll Hanoi 100000 Vietnam

In this study, a new methodology, hybrid NSGA-III with multi-objective particle swarm optimization (HNSGA-III&MOPSO), has been developed to design and achieve cost optimization of Powertrain mount system stiffness parameters. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration and mean square displacement of the Powertrain mount system. A hybrid HNSGA-III&MOPSO is proposed with the integration of multi-objective particle swarm optimization and a genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&MOPSO is more efficient than the typical multi-objective particle swarm optimization, NSGA-III. Powertrain mount system stiffness parameter optimization with HNSGA-III&MOPSO is simulated, respectively. It proved the potential of the HNSGA-III&MOPSO for Powertrain mount system stiffness parameter optimization problem. The amplitude of the acceleration of the vehicle frame decreased by 22.8%, and the amplitude of the displacement of the vehicle frame reduced by 12.4% compared to the normal design case. The calculation time of the algorithm HNSGA-III&MOPSO is less than the algorithm NSGA-III, that is, 5 and 6 h, respectively, compared to the algorithm multi-objective particle swarm optimization.

关键词： NSGA-III algorithm multi-objective particle swarm optimization algorithm optimal solution Powertrain mount system stiffness multi-objective evolutionary algorithms mounting system

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